Holographic data storage systems store information or data based on the concept of a signal beam interfering with a reference beam at a holographic storage medium. The interference of the signal beam and the reference beam creates a holographic representation, i.e., a hologram, of data elements as a pattern of varying refractive index and/or absorption imprinted in a volume of a storage or recording medium such as a photopolymer or photorefractive crystal. Combining a data-encoded signal beam, referred to as an object beam, with a reference beam creates the interference pattern at the storage medium. A spatial light modulator (SLM), for example, can create the data-encoded signal beam. The interference pattern induces material alterations in the storage medium that generate the hologram. The formation of the hologram in the storage medium is a function of the relative amplitudes and polarization states of, and phase differences between, the signal beam and the reference beam. The hologram is also dependent on the wavelengths and angles at which the signal beam and the reference beam are projected into the storage medium. After a hologram is created in the storage medium, projecting the reference beam into the storage medium reconstructs the original data-encoded signal beam. The reconstructed signal beam may be detected by using a detector, such as CMOS photo-detector array or the like. The detected data may then be decoded into the original encoded data.
In a page-oriented holographic data storage device, it is advantageous to minimize the size of the holograms in order to achieve maximum storage density. One method of accomplishing this is minimizing the size of the page imaging aperture. However, minimizing the size of the aperture has the consequence of increasing blur, in terms of broadening the pixel spread function (PSF) in the page images. This blur decreases the signal-to-noise ratio (SNR) of the holographic storage device, which increases the bit error rate (BER) of the system, and which in turn limits the storage density.
Since blur in an image is a deterministic process, much of the SNR loss may be reclaimed by digitally post-processing the detected page image. Traditionally, the detected image is convolved with a small kernel matrix w, also known as a kernel, representing an inverse blurring operation (de-convolution), thereby implementing a finite impulse response (FIR) filter equalization.
The kernel of a FIR filter, for example a 3×3 or a 5×5 matrix, may be determined by several methods known in the current art. For example, if the page image pixel spread function is known, a zero-forcing equalizer may be designed by calculating the linear inverse of the PSF. An example of the zero-forcing method is described in “Channel estimation and intra-page equalization for digital volume holographic data storage,” by V. Vadde and B. Kumar in Optical Data Storage 1997, pp. 250-255, 1997. Another approach is to choose FIR filter coefficients that minimize the difference between the equalized data page image and the original data page. Such a method is described in “Application of linear minimum mean-squared-error equalization for volume holographic data storage,” by M. Keskinoz and B. Kumar in Applied Optics, vol. 38, no. 20, Jul. 10, 1999.
Performance of FIR equalization as shown in the prior art is limited in at least two aspects. First, blur in a coherent imaging system is not a linear process. Although coherent light adds linearly in electric field strength, detectors can only directly detect irradiance. This introduces a nonlinear absolute value squared transformation. Furthermore, each detector element (pixel) integrates the irradiance over an area, introducing a further nonlinearity. Prior art has disclosed ways to solve this problem either through a “magnitude model” (operating on the square root of the detected values, but lacking phase information), or through an “intensity model” (operating on the PSF and the pixel fill factors). An example of both the “magnitude model” and the “intensity model” is described in “Channel modeling and estimation for intra-page equalization in pixel-matched volume holographic data storage,” by V. Vadde and B. Kumar in Applied Optics, vol. 38, no. 20, Jul. 10, 1999.
Second, the performance of FIR equalization described by the prior art is limited because real imaging systems are not perfect shift invariant linear systems. In other words, the pixel spread function is not constant at all locations in the field of view. There are a number of factors that create variations in the width or shape of the PSF throughout the field of view. For example, variations may be caused by lens aberrations and misalignment; by distortions, shrinkage, and other non-ideal media responses; and by misalignment and wavefront errors in the reconstructing reference beam. A significant consequence of these effects in a pixel-matched system is the degradation of the pixel matching, because image distortion shifts local areas of the image with respect to the detector pixels. For example, a uniform shrinkage of the medium causes the holographic image to be magnified, producing a radial displacement such that data pixel images are no longer centered on their respective detector pixels.
Therefore, new methods and systems for addressing the issues of the prior art methods are needed. In particular, methods and systems for equalizing holographic image data are needed to improve the storage density of the holographic data storage system. Further, methods and systems for compensating nonlinearity of the holographic data storage channel are also needed to improve the storage density of the holographic data storage system.